使用深度神经网络预测由于遗传倾向的不良药物反应
Bryan Dafniet1, Olivier Taboureau1
1INSERM U1133, CNRS UMR 8251, Université Paris Cité, 35 rue Hélène Brion, Paris, 75013, France.
Molecular informatics
|June 8, 2024
概括
这项研究引入了一种新的深度学习模型,用于预测与药物不良反应 (ADR) 相关的遗传变异. 该方法使用突变数据来识别潜在的药物安全问题,帮助精准医学.
科学领域:
- 药物基因组学 药物基因组学
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 由于毒性和药物不良反应 (ADRs),药物开发面临重大挑战,这些药物可能受到个体遗传变异的影响.
- 全基因组关联研究 (GWAS) 已经确定了与药物反应相关的遗传变异,但预测ADR仍然是复杂的.
研究的目的:
- 研究一种深度学习方法来预测可能与药物不良反应 (ADRs) 相关的遗传变异.
- 开发和评估深度神经网络 (DNN) 模型,以识别与ADR相关的遗传因素.
主要方法:
- 利用dbSNP的单核酸多态 (SNP) 数据,构建一个集成ADR药物向突变信息的网络.
- 提取交互矩阵来构建深度神经网络 (DNN) 模型.
- 纳入影响药物有效性/安全性的突变数据 (PharmGKB) 和ADRs (MedDRA系统器官类别).
主要成果:
- 开发的DNN模型在预测ADR相关的遗传变异方面实现了0.61的平均平衡精度.
- 包括分子指纹并没有提高模型性能,这表明主要价值在于遗传和突变数据.
- 这代表了第一个使用DNN来预测ADR药物标突变的已知模型.
结论:
- 深度学习模型显示出分析遗传多态度和预测潜在的ADRs的前景.
- 这些模型可以促进对众多化合物和遗传变异的分析,为精准医学的进步做出贡献.
- 建议进一步改进,但目前的模型为药物安全性评估提供了有价值的工具.
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